Jocelyn CHANUSSOT
Professeur Grenoble-INP
Equipe SIGnal iMAge PHYsique
Département Images et Signal
ME CONTACTER / CONTACT ME
Mail : jocelyn.chanussot@gipsa-lab.grenoble-inp.fr

11 rue des mathématiques
Domaine Universitaire
BP 46
38402 Saint Martin d'Hères cedex

Bureau D1136
Tél.33 (0)4 76 82 62 73
Fax : 33 (0)4 76 57 47 90
PUBLICATIONS RECENTES / RECENT PUBLICATIONS
Les derniéres publications de la collection Gipsa dans HAL

Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification

Danfeng Hong, Naoto Yokoya, Nan Ge, Jocelyn Chanussot, Xiao Xiang Zhu. Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification. ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, 2019, 147, pp.193-205. 〈10.1016/j.isprsjprs.2018.10.006〉. 〈hal-01961357〉

Dynamic Multi-Scale Segmentation of Remote Sensing Images based on Convolutional Networks

Keiller Nogueira, Mauro Dalla Mura, Jocelyn Chanussot, William Robson Schwartz, Jefersson Alex Dos Santos. Dynamic Multi-Scale Segmentation of Remote Sensing Images based on Convolutional Networks. 2018. 〈hal-01961078〉

An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing

Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu. An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing. 2018. 〈hal-01961090〉

Target Recognition in SAR Image via Keypoint based Local Descriptor—Foundation

Ganggang Dong, Jocelyn Chanussot. Target Recognition in SAR Image via Keypoint based Local Descriptor—Foundation. 2018. 〈hal-01961409〉

Remote Sensing Data Fusion: Guided Filter-Based Hyperspectral Pansharpening and Graph-Based Feature-Level Fusion

Wenzhi Liao, Jocelyn Chanussot, Wilfried Philips. Remote Sensing Data Fusion: Guided Filter-Based Hyperspectral Pansharpening and Graph-Based Feature-Level Fusion. Mathematical Models for Remote Sensing Image Processing, 8 (3), pp.243-275, 2018. 〈hal-01961387〉

Deep Learning for Fusion of APEX Hyperspectral and Full-Waveform LiDAR Remote Sensing Data for Tree Species Mapping

Wenzhi Liao, Frieke Van Coillie, Lianru Gao, Liwei Li, Bing Zhang, et al.. Deep Learning for Fusion of APEX Hyperspectral and Full-Waveform LiDAR Remote Sensing Data for Tree Species Mapping. IEEE Access, IEEE, 2018, 6, pp.68716-68729. 〈hal-01960716〉

A Combiner-Based Full Resolution Quality Assessment Index for Pansharpening

Gemine Vivone, Paolo Addesso, Jocelyn Chanussot. A Combiner-Based Full Resolution Quality Assessment Index for Pansharpening. IEEE Geoscience and Remote Sensing Letters, IEEE - Institute of Electrical and Electronics Engineers, 2018, pp.1-5. 〈hal-01960791〉

Snow Cover Estimation From Image Time Series Based on Spectral Unmixing

Théo Masson, Mauro Dalla Mura, Marie Dumont, Jocelyn Chanussot. Snow Cover Estimation From Image Time Series Based on Spectral Unmixing. IEEE Geoscience and Remote Sensing Letters, IEEE - Institute of Electrical and Electronics Engineers, 2018, pp.1-5. 〈hal-01960797〉

Hyperspectral Imagery for Environmental Urban Planning

Christiane Weber, Thomas Houet, Sébastien Gadal, Rahim Aguejdad, Grzegorz Skupinski, et al.. Hyperspectral Imagery for Environmental Urban Planning. [Research Report] CNRS UMR TETIS, ESPACE, LETG, ONERA, GIPSA-lab, IRAP, IGN. 2018. 〈hal-01930658〉

Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation

Fahim Irfan Alam, Jun Zhou, Alan Wee-Chung Liew, Xiuping Jia, Jocelyn Chanussot, et al.. Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2018, pp.1-17. 〈10.1109/TGRS.2018.2867679〉. 〈hal-01687733〉

ENCADREMENT DE THESES / PhD THESIS SUPERVISED

Grenoble Images Parole Signal Automatique laboratoire

UMR 5216 CNRS - Grenoble INP - Université Joseph Fourier - Université Stendhal